Summary of Uncertainty Separation Via Ensemble Quantile Regression, by Navid Ansari et al.
Uncertainty separation via ensemble quantile regression
by Navid Ansari, Hans-Peter Seidel, Vahid Babaei
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel and scalable framework for uncertainty estimation and separation is introduced, which has applications in data-driven modeling for science and engineering tasks where reliable uncertainty quantification is critical. The approach uses an ensemble of quantile regression (E-QR) models to enhance aleatoric uncertainty estimation while preserving epistemic uncertainty, outperforming competing methods like Deep Ensembles (DE) and Monte Carlo (MC) dropout. To separate uncertainty types, the algorithm iteratively improves separation through progressive sampling in regions of high uncertainty. The framework is scalable to large datasets and demonstrates superior performance on synthetic benchmarks, offering a robust tool for uncertainty quantification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to figure out how certain you are about things when using data to make predictions. It’s like trying to guess what the weather will be tomorrow – sometimes it’s hard to say exactly what will happen! The new method uses lots of little models that all try to guess the same thing, and it helps us know how sure we can be about our answers. This is important because in science and engineering, knowing how certain you are can make a big difference. |
Keywords
» Artificial intelligence » Dropout » Regression
